By Stefano Ceri, Piero Fraternali, Stefano Paraboschi (auth.), Carlo Zaniolo, Peter C. Lockemann, Marc H. Scholl, Torsten Grust (eds.)
EDBT 2000 is the 7th convention in a sequence devoted to the development of database expertise. This year’s convention exact subject, \Connect hundreds of thousands of clients and information Sources," underscores the significance of databases for the data age that's dawning with the hot millennium. the significance - rives not only from the statement that the data age basically rests on theconvergenceofcommunications,computing,andstorage.Equallyimportant, a few of the recommendations and methods underlying the luck of databasesystems have autonomous which means and effect for today’s disbursed details s- tems. The papers within the quantity must also be obvious during this mild. The EDBT 2000 convention application comprises 30 examine papers chosen by means of this system committee out of 187 submissions, protecting advances in learn, improvement, and functions of databases. The convention software additionally - cludes six andapplications papers,a panel discussion,six tutorials,and a number of software program demonstrations. The convention positive factors 3 unusual - vited audio system: Ashish Gupta discusses database matters in digital trade, Stefano Ceri addresses the effect and demanding situations of XML on databases, and Andreas Reuter stocks his perspectives on new views on database expertise. The technical contributions offered on the EDBT 2000 convention are colle- ed and preserved during this quantity that we're happy to give to you with the expectancy that it'll function a useful examine and reference software on your specialist life.
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Extra resources for Advances in Database Technology — EDBT 2000: 7th International Conference on Extending Database Technology Konstanz, Germany, March 27–31, 2000 Proceedings
Moreover, if the following inequality holds for two points p and p , p cannot be the NN. uBnd 2 (p, q) < lBnd 2 (p , q) ⇒ p = nn(q) (3) NN-Search. The search algorithm has two phases: the first phase (filtering step) scans the approximations of the vectors to determine lower and upper bounds on the distance between the current vector and q. Using Equation (3), we can prune vectors: if the lower bound of a vector is larger than the smallest upper bound seen so far, the vector cannot be the NN. In the second phase (refinement step), we visit the remaining vectors sorted by the lower bounds in increasing order.
Thus, M also measures the quality of approximation. We derive a formula for M under the assumption that the distance distribution is the same from each point of the data space (homogeneity assumption ). Note that this assumption is more general than the uniformity assumption in the previous subsection. Thus, our estimation of M applies to real data sets as well. Expected Nearest Neighbor Distance. A good estimation of the NNdistance of a query is the expected NN-distance of the data set over all possible queries.
Schek and Martin Breunig for helpful comments. References 1. Sunil Arya et al. An Optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions. Technical report, 1998. 2. D. Barbara, W. DuMouchel, C. Faloutsos, P. J. M. Hellerstein, Y. V. Jagadish, T. Johnson, R. Ng, V. A. Ross, and K. C. Sevcik. The New Jersey data reduction report. Data Engineering, 20(4):3–45, 1997. 3. N. -P. Kriegel, R. Schneider, and B. Seeger. The R*-tree: An efficient and robust access method for points and rectangles.
Advances in Database Technology — EDBT 2000: 7th International Conference on Extending Database Technology Konstanz, Germany, March 27–31, 2000 Proceedings by Stefano Ceri, Piero Fraternali, Stefano Paraboschi (auth.), Carlo Zaniolo, Peter C. Lockemann, Marc H. Scholl, Torsten Grust (eds.)